Conference paper Open Access

Fairness in Proprietary Image Tagging Algorithms: A Cross-Platform Audit on People Images

Kyriakou Kyriakos; Barlas Pınar; Kleanthous Styliani; Otterbacher Jahna

There are increasing expectations that algorithms should behave in a manner that is socially just. We consider the case of
image tagging APIs and their interpretations of people images. Image taggers have become indispensable in our information
ecosystem, facilitating new modes of visual communication and sharing. Recently, they have become widely available as Cognitive Services. But while tagging APIs offer developers an inexpensive and convenient means to add functionality to their creations, most are opaque and proprietary. Through a cross-platform comparison of six taggers, we show that behaviors differ significantly. While some offer more interpretation on images, they may exhibit less fairness toward the depicted persons, by misuse of gender-related
tags and/or making judgments on a person’s physical appearance. We also discuss the difficulties of studying fairness in situations where algorithmic systems cannot be benchmarkedagainst a ground truth.

This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 739578 and under Grant Agreement No 810105 and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.
Files (1.3 MB)
Name Size
ICWSM_tagging.pdf
md5:2e6c32301a60b76c422fcc28f48ba07e 		1.3 MB Download
45
35
views
downloads
All versions This version
Views 4545
Downloads 3535
Data volume 47.0 MB47.0 MB
Unique views 4141
Unique downloads 3333
More info on how stats are collected.
Indexed in
Publication date:
October 30, 2019
DOI:
10.5281/zenodo.3522950

Zenodo DOI Badge

DOI 

10.5281/zenodo.3522950

Markdown 

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3522950.svg)](https://doi.org/10.5281/zenodo.3522950)

reStructedText 

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3522950.svg
   :target: https://doi.org/10.5281/zenodo.3522950

HTML 

<a href="https://doi.org/10.5281/zenodo.3522950"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.3522950.svg" alt="DOI"></a>

Image URL 

https://zenodo.org/badge/DOI/10.5281/zenodo.3522950.svg

Target URL 

https://doi.org/10.5281/zenodo.3522950

ISBN:
978-1-57735-806-0
Keyword(s):
image tagging APIs
Published in:
Proceedings of the International AAAI Conference on Web and Social Media, 13(01), Association for the Advancement of Artificial Intelligence, Menlo Park, California, United States, pp. 313-322 (978-1-57735-806-0).
Grants:
European Commission:
  • RISE - Research Center on Interactive Media, Smart System and Emerging Technologies (739578)
  • CyCAT - Cyprus Center for Algorithmic Transparency (810105)
Communities:
License (for files):
Creative Commons Attribution Non Commercial No Derivatives 4.0 International

Versions

Version Published 10.5281/zenodo.3522950 Oct 30, 2019
Cite all versions? You can cite all versions by using the DOI 10.5281/zenodo.3522949. This DOI represents all versions, and will always resolve to the latest one. Read more.

Share

Cite as

Export